Log Processing

ABSTRACT

Set top box logs are received from a television provider and processed to identify channel tunes and channel tune times. The channel tunes and channel tune times of the log data can be compared to expected air times of television advertisements on channels, and an impression values, e.g., projected viewers, can be generated for each television advertisement.

This application claims the benefit of U.S. Application Ser. No.60/909,893, entitled “Television Advertising,” filed on Apr. 3, 2007;U.S. Application Ser. No. 60/915,261, entitled “Log Processing,” filedon May 1, 2007; and U.S. Patent Application Ser. No. 60/944,992,entitled “Log Processing,” filed on Jun. 19, 2007; the disclosures ofwhich are incorporated herein by reference.

BACKGROUND

This disclosure relates to media advertising.

An advertiser, such as a business entity, can purchase airtime during atelevision broadcast to air television advertisements. Exampletelevision advertisements include commercials that are aired during aprogram break, transparent overlays that are aired during a program, andtext banners that are aired during a program, product placements in aprogram, etc.

The cost of the airtime purchased by the advertiser varies according tothe audience size and audience composition expected to be watchingduring the purchased airtime or closely related to the purchasedairtime. The audience size and audience composition, for example, can bemeasured by a ratings system. Data for television ratings can, forexample, be collected by viewer surveys in which viewers provide a diaryof viewing habits; or by set meters that automatically collect viewinghabit data and transmit the data over a wired connection, e.g., a phoneline or cable line; or by digital video recorder service logs, forexample. Such rating systems, however, may be inaccurate for nicheprogramming, and typically provides only an estimate of the actualaudience numbers and audience composition.

Based on the ratings estimate, airtime is offered to advertisers for afee. Typically the advertiser must purchase the airtime well in advanceof the airtime. Additionally, the advertiser and/or the televisionprovider may not realize the true value of the airtime purchased if theratings estimate is inaccurate. Finally, if the advertiser and/or thetelevision provider participates in a revenue agreement based on thenumber of identified viewings of an advertisement, accurate datarelating to the potential viewing audience during the time theadvertisement aired must be obtained in order to produce a fair result.

SUMMARY

Described herein are systems and methods for processing logs oftelevision devices, e.g., set top boxes, digital video recorders, etc.In one implementation, impression records are received from a televisionprovider. The impression records defining channel identifiers,associated tuning events and associated tuning times reported by tuningdevices. The impression records are filtered to identify false positiveimpression records.

In another implementation, reporting data are received from a televisionprovider, and channel tunes and corresponding tune times from thereporting data are identified. Television advertisements that were airedduring the tune times on channels corresponding to the channel tunes areidentified. Reliable durations based on the identified channel tunes andcorresponding tune times, and impression values for each identifiedtelevision advertisement based on the reliable durations are determined.

In another implementation, a system includes an impression processor andan impression filter. The impression processor can be configured toreceive impression records from a television provider and televisionprovider metadata. The impression records can identify channelidentifiers, associated tuning events and associated tuning timesreported by tuning devices, and the television provider metadata caninclude insertion times and intended display times of televisionadvertisements. The impression processor can also be configured toadjust the associated tuning times to compensate for televisionprocessing latency, and generate normalized impression records. Eachnormalized impression record can include a television provideridentifier, an insertion identifier, and a time duration. The impressionfilter can be configured to compare the normalized impression records tofiltering rules and identify reliable normalized impression recordsbased on the comparison.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example television advertisement system.

FIG. 2 is a block diagram of an example television advertisement systemfront end.

FIG. 3 is a block diagram of an example television advertisementdistribution system.

FIG. 4 is a block diagram of an example television advertisementscheduling and reporting system.

FIG. 5 is an environment for an example log processing system.

FIG. 6A is an example implementation of a log processing system in atelevision advertising system.

FIGS. 6B-6D are example dwell time plots.

FIG. 6E is an example plot of channel tunes during a broadcast timeperiod.

FIG. 7 is a flow diagram of an example process for processing logs anddetermining impressions from the processed logs.

FIG. 8 is a flow diagram of an example process for adjusting log data tocompensate for broadcast delays.

FIG. 9 is a flow diagram of an example process for identifying reliabledurations and false positive durations.

FIG. 10 is a flow diagram of another example process for processing logsand determining impressions from the processed logs.

FIG. 11 is a flow diagram of another example process for processing logsand determining impressions from the processed logs.

FIG. 12 is a flow diagram of an example process for estimating animpression total from log data.

FIG. 13 is a flow diagram of an example process for iterativelyprocessing logs.

FIG. 14 is a flow diagram of an example process for adjustingadvertising costs based on automatically generated channel tune times.

FIG. 15 is a flow diagram of an example process for identifyingautomatically generated channel tunes.

FIG. 16 is a flow diagram of an example process for adjustingadvertising costs based on identified channel tune clusters.

FIG. 17 is a flow diagram of another example process for identifyingautomatically generated channel tunes.

FIG. 18 is a flow diagram of another example process for identifyingautomatically generated channel tunes.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of an example television advertisement system100. The television advertising system 100 can, for example, deliverrelevant content items (e.g., television advertisements, and hereinafterreferred to generally as advertisements) advertisements to viewers tofacilitate operator monetization of programming and quantification ofadvertisement delivery to target markets. The television advertisingsystem 100 can, for example, be implemented on one or computer servers,and can provide and receive data over a network. Example networksinclude local area networks (LANs), wide area networks (WANs),telephonic networks, and wireless networks (e.g., 802.11x compliantnetworks, satellite networks, cellular networks, etc.). Additionally,the television advertising system 100 can, for example, communicate overseveral different types of networks, e.g., the Internet, a satellitenetwork, and a telephonic network.

In general, the television advertising system 100 can receive televisionadvertisements and advertisement campaign data from an advertiser 140,e.g. an entity that provides television advertisements, such as acommercial entity that sells products or services, an advertisingagency, or a person. The television advertising system 100 canfacilitate the provisioning of television advertisements to a televisionprovider 160, e.g., an entity that facilitates the delivery of atelevision broadcast to viewers, such as cable provider, a digitalsatellite provider, a streaming media provider, or some other mediaprovider. The provider 160 can, for example, receive a provider agent162 from the television advertisement system 100. The provider agent 162can, for example, be located at an advertising broadcast insertionlocation of the provider 162, e.g., at a head end of the provider.

The provider agent 162 can, for example, receive advertisementavailability schedules from the provider 160 and provide theadvertisement availability schedules to the television advertisingsystem 100. Example advertisement availability schedules includescheduling data related to advertisement spots, times slots, pods(groups of time slots), screen real estate (e.g., a region in a textbanner or screen corner for an overlay), etc. For example, the provideragent 162 can read schedule requests, either in real time or ahead oftime, and identify which schedule times the television advertisingsystem 100 has permission to fill with advertisements provided by thetelevision advertising system 100. Alternatively, the provider agent 162can determine if one or more advertisements already scheduled orshould/can be preempted or receive information that a scheduledadvertisement should be preempted in accordance with one or morecriteria (e.g., to improve revenue generation for the provider, or if anadvertiser's budget has been depleted after a related advertisement wasscheduled, etc.). The provider agent 162 can request the televisionadvertising system 100 to identify a relevant advertisement for anidentified airtime advertisement spot, e.g., an open advertisement slotor a preempted advertisement slot.

The advertisement can be deemed relevant based on advertisement metadata and an advertisement context, e.g., an advertisement for extremesporting equipment for an advertisement having an available advertisingbudget may be selected for airing during a televised sporting event forwhich the meta data identifies as a primary demographic 18-30 year oldmales.

The television advertising system 100 can, for example, select candidateadvertisements to air during an advertisement availability based onaccount advertiser bids, budgets, and any quality metrics that have beencollected, e.g., conversions, viewer actions, impressions, etc. Forexample, advertisements can be selected to air during the advertisementavailability according to a computer-implemented auction. One examplingauction is a Vickrey-style in which each advertiser pays the bid of thenext highest advertisement. Other auction processes can also be used,e.g., setting an advertiser bid equal to the estimated number of viewerimpressions multiplied by the price an advertiser has offered to pay foreach impression, etc.

Different bidding types can be implemented in the computer-implementedauction. For example, the bidding types can be cost per airing, a costper impression, a cost per full viewing of the advertisement, a cost perpartial viewing of the advertisement, etc. Other types of costs peractions can also be use, such as a phone calls resulting from phone callsolicitations; a cost per network airing (e.g., $5.00 per 1000impressions on a first network, $6.00 per 1000 impressions on a secondnetwork), cost per action scaled by the time of day, etc. An auctionprocess can, for example, support ads with different or even multiple(hybrid) bidding types.

The advertisements selected from the television advertising system 100auction, the advertisement air time, and/or the advertisement can beprovided to the provider 160. For example, all available advertisements,or a subset thereof, can be provided to the provider 160 prior toairing, and the provider agent 162 need only receive an advertisementidentifier indicating which advertisement is to air during particularadvertisement air time.

The provider agent 162 can thereafter provide a status to the televisionadvertisement system 100 regarding when the advertisement aired. Theprovider agent 162 can also, for example, provide anonymized impressiondata related to viewing devices 164 a-164 n. Example viewing devicesinclude set top boxes, digital video recorders and tuners, and othertelevision processing devices that facilitate the viewing of thetelevision signal on a television device. For example, logs related toviewing device 164 activity, e.g., set top box logs, can be anonymizedto remove personal information related to viewing activities andprovided to the television advertising system 100. In anotherimplementation, such information can be provided by the provider 160, orby a third party.

In one implementation, based on the impression data for the airing ofthe advertisement, the television advertising system 100 can charge anadvertiser a fee for airing the advertisement. The fee can, for example,be substantially in proportion to the number of impressions determinedfor a particular airing of an advertisement.

In one implementation, the fee can, for example, be based on the biddingtype. For example, the bid may be based on a cost per airing, and thusan advertiser would be charged a fee for the airing of theadvertisement. Other fee determinations can also be used.

The impressions can, for example, be measured statistically. Animpression can be a household impression, e.g., the airing of anadvertisement in household and independent of the number of televisionsin a household. If the advertisement is aired on a viewing device in thehousehold, one household impression can be recorded. Other impressiontypes can also be used. For example, impressions can be generated by aprogram rating percentage, e.g., a percentage of viewership inmeasurable households; or by a program share percentage, e.g., apercentage of viewership in active measured homes; or by some otherstatistical measurement.

By way of another example, impressions can be measured by an analysis ofactivity logs of the viewing devices 164. For example, a household mayhave three viewing devices 164, and at a given time two of the devicesmay be tuned to a first channel and the third device may be tuned to asecond channel. If a first commercial airs on the first channel and asecond commercial airs on the second channel, impressions can begenerated for each viewing device.

An impression can be dependent on a channel tune status when anadvertisement airs on a channel. For example, an impression can occurwhen a viewing device 164 is tuned to a broadcast stream in which anadvertisement is inserted, and the viewing device 164 remains tuned tothe broadcast stream for N consecutive seconds during the actual displaytime of the insertion. For example, an impression can be defined as aviewing device remaining tuned to a broadcast stream for five secondsafter the advertisement begins to air. Alternatively, an impression canbe defined as a viewing device tuned to a broadcast stream when anadvertisement is airing and remaining tuned to the broadcast stream forfive seconds after tuning to the broadcast stream. Other tune times canalso be used.

Likewise, other impression types can also be used. For example, animpression can based on an advertisement exposure, e.g., a briefexposure of an advertisement, or a full viewing of the advertisement, ofa threshold viewing in between, e.g., five seconds, or five seconds ofthe first fifteen seconds; or a percentage of the advertisement viewed,etc.

In an implementation, the television advertisement system 100 can alsoinclude one or more data stores to store television advertisements andassociated data, e.g., meta data related to the televisionadvertisements, performance data related to the televisionadvertisements, accounting data related to the advertisers andtelevision advertisements, etc. In one implementation, the televisionadvertising system 100 includes an advertisement data store 102, anadvertisement parameter store 104, a log data store 106, a performancedata store 108, and an out of band data store 110. Additionaladvertisement related data can also be stored, e.g., an accounting datastore 112 can store accounting data.

The television advertisement data store 102 can, for example, includeadvertisements that can be broadcast or aired during an advertisementspot. Example television advertisements include video advertisements,banner advertisements, overlay advertisements, such as logos, URLs,dynamic pricing information for an advertisement, etc., and otheradvertisements that can be used to convey information visually and/oraurally during a television broadcast.

The television advertisement parameter data store 104 can, for example,include cost per action parameters, frequency values, competitiverestrictions, advertising budget data, geographic data, targeting data,etc. The television advertisement parameters 104 can, for example, bespecified by the advertiser 140, and/or can be automatically updatedbased on the performance of advertisements during an advertisementcampaign.

The log data store 106 can include data logs from viewing devices 164,e.g., set top boxes, satellite receivers, etc. The log data can includereporting data that identifies channel tunes, e.g., a channel identifierto which the viewing device was tuned, such as may occur which theviewing device 164 is processing video data to record and/or display,and channel tune times, e.g., the times that the viewing device wastuned to a channel. Other data can also be included, e.g., key pressesof remote devices associated with the viewing devices 164, commandsreceived by the viewing devices 164, etc. For example, if the viewingdevice 164 is a digital video recorder, the log data can include a listof recorded programs, and for each recorded program a record thatindicates whether the recorded program has been played back, and theactions taken during playback, such as fast forwarding or skippingcommercials can be included.

The performance data store 108 can, for example, include quality data,e.g., a total number of impressions for each advertisement, or animpression rate for each advertisement, and/or other quality parameterand/or impression parameters. Example impression rates include apercentage of total potential viewers, e.g., the number of identifiedimpressions divided by the number of subscribers; a percentage of actualimpressions of a total number of impressions, e.g., a percentage ofreliable impressions divided by a total number of impressions. Anexample reliable impression is an impression that satisfies a rule setor condition that determines that the impression was likely viewed on aviewing device by one or more persons.

Other performance data can also be stored in the performance data store108, e.g., performance of a particular advertisement during particularprogramming, the probability that viewers will tune to another channelduring an advertisement; the probability that viewers will fast-forwardthrough the advertisement; etc. Such probabilities can be normalized toaverage behavior on a per-advertisement basis, or on a per-time slotbasis, or on some other measurable basis.

Performance data can also include data related to how much of anadvertisement a viewer watched when the advertisement aired. Forexample, statistics related to aggregate tune-in and tune-out times;number of viewers, etc. can be measured and evaluated to determinequality data related to advertisements based on viewing percentages.

The out of band data store 100 can, for example, include data related tothe relevance or popularity of particular advertisements, advertisementsubject matter, and television programs. For example, web pages can bemined to determine whether particular television programs are expectedto have increased viewership, e.g., a sudden increase in fan pages for aprogram can be correlated to an increase in an expected ratings value,and the resulting data can be stored in the out of band data store 110.Other data can include data related to search queries, page views on anadvertise site, etc.

Likewise, the out of band data store 110 can, for example, store dateresulting from mining a video web site to identify televisionadvertisements that are particularly popular, e.g., a video web site mayrecord viewing statistics that indicate a particularly humorousadvertisement is relatively popular among a certain demographic. Suchdata can be used to further refine the advertising selection process.

Additionally, web sites related to television programs can be mined todetermine relevance of products or services related to the televisionprogram. For example, a particular program may reference a product in anepisode, and the mining of fan sites related to the program may revealthat the product mention has generated a significant interest in theproduct and related products. Accordingly, advertisements related to theproduct and related products may be deemed more relevant for time slotsduring the program.

The accounting data store 112 can, for example, store accounting datarelated to advertisements and advertisers 140. The accounting data store112 can store such data as campaign budgets, monthly spend parameters,and account balances for advertisers.

Other data can also be stored, such as data that can be utilized toadjust viewing forecasts, adjust pricing models, adjust relevancymeasures, etc. For example, performance data related to certain productsor services advertised, e.g., anonymized historical campaign data, trendanalysis of program viewership, e.g., viewing statistics of programseries episodes in first run, repeat, and syndication, etc. can bestored for analysis. In one implementation, data related toadvertisements that were aired during time slots not served by theadvertisement system 100 can be stored to analyze other advertisingmarket models, e.g., fixed priced advertising markets, reservedadvertising markets, etc.

The advertisement data store 102, advertisement parameter store 104, logdata store 106, performance data store 108, the out of band data store110 and the accounting data store 112 can be implemented separately orin combination. For example, in one implementation, the advertisementdata store 102, advertisement parameter store 104 and performance datastore 108 can be implemented in a single advertisement database. Othercombinations and/or subcombinations can also be used.

The television advertisement system 100 can include an advertisementfront end engine 120, an advertisement distribution engine 122, ascheduler engine 124, a candidate engine 126, a reporting engine 128,and an accounting engine 130. The advertisement front end engine 120,advertisement distribution engine 122, scheduler engine 124, candidateengine 126, reporting engine 128, and accounting engine 130 can, forexample, be distributed among a plurality of computer devices, e.g.server computers communicating over a network, or can be implemented ona single computer, e.g., as multiple threads on a server computer. Otherimplementation architectures can also be used. The advertisement frontend engine 120, advertisement distribution engine 122, scheduler engine124, candidate engine 126, reporting engine 128, and accounting engine130 can, for example, be implemented in software, such as executableobject code, interpreted script instructions, or in combinations ofexecutable and interpreted instructions. Other software and/or hardwareimplementations can also be used.

The advertisement front end engine 120 can, for example, be configuredto receive advertisement data and television advertisements from theadvertiser 140 and associate the advertisement data with the televisionadvertisements. In one implementation, the advertisement front endengine 140 can include a web-based interface through which theadvertiser 140 can upload television advertisements and associatedcampaign data, e.g., advertising budgets, targeting data, such asdemographics and air times, product and/or service description data,such as vertical classifications, price ranges, subject matter, etc.

In one implementation, the advertisement front end engine 120 caninclude an approval engine configured to identify a televisionadvertisement pending approval by the television provider 160. Utilizingthe approval engine 160, the publisher may optionally review anadvertisement and either approve or disapprove of the advertisement. Forexample, a cable provider may disapprove of advertisements that are ofparticularly low quality, e.g., poor sound quality, incorrectadvertisement data, etc.

The advertisement distribution engine 122 can, for example, beconfigured to provide approved advertisements to the television provider160. In one implementation, the advertisements are provided to thetelevision provider 160 in advance of airing the advertisements. Theprovider agent 162 can periodically issue a request to the televisionadvertising system 100 for any new advertisements to be downloaded. Forany such advertisements, the provider agent 162 or the distributionengine 122 can initiate the download, and upon successful completion theprovider agent 162 can notify the television advertising system 100 of asuccessful download. The television advertising system 100 can, forexample, label the download with a particular ID that can be later userduring scheduling to identify the scheduled advertisement. Accordingly,the publisher 160 can receive an advertisement identifier associatedwith advertisement availability, e.g., a time slot, and can retrieve theadvertisement locally at the television provider 160 premises and insertthe selected advertisement into the broadcast stream.

The television advertisement scheduler engine 124 can, for example, beconfigured to receive a television advertisement request definingtelevision advertisement availability from the television provider 160,and issue a request for candidate television advertisement data, e.g.,data related to advertisements that are candidates for being selected tofill the advertisement availability. The television advertisementrequest can include geographic data, provider identification, networkdata, program data, and other data. For example, a request can specifyadvertisements that can be shown in the geographic area of theUSA/California/Bay Area/Mountain View, with a remoteRepositoryId of XXof a television provider, for a television provider YY, on a televisionnetwork ZZ, to be scheduled within the time window of Monday 2:00PM-3:00 PM, and at a preferred time of 2:16 PM.

The candidate engine 126 can, for example, be configured to identifycandidate television advertisement data in response to the request forcandidate television advertisement data. The identification can be basedon data associated with the television advertisements, such as the datastored in the advertisement parameter data store 104. The candidateengine 126 can implement various targeting and/or filtering rules. Forexample, a budget restriction can be imposed if an advertiser budget isnearly depleted, and the expected fee for airing the advertisement basedon expected impressions would exceed the remaining advertising budget.

Other example rules include the advertisement being successfullydownloaded to the television provider 160; the advertisement targetingthe location or a superset of the location where the advertisement willbe showing; the advertiser 140 or advertisement must not be consideredfraudulent or delinquent; the publisher 160 has approved theadvertisement for showing; the advertisement is targeting thisparticular television network and/or time; the advertisement istargeting a television program which, through internal or third partydata sources, corresponds to the given request; and the advertisement istargeting a demographic profile which, through internal or third partydata sources, corresponds to the given request. Fewer or more filteringand targeting conditions can also be applied.

In response to receiving the candidate advertisement data, thetelevision advertisement scheduler engine 124 can select one or moretelevision advertisements to air during the television advertisementavailability. The selection can be based on the television advertisementrequest and the candidate television advertisement data. For example,the television advertisement request can be utilized to determine acontext, e.g., the context of the programming associated with theadvertisement, such as sporting event, an entertainment genre, a newsprogram, etc.; or the context of the television network, e.g., a networktype; or the context of a television channel; or the context of the timeof day; or a combination of any of such examples. The context can beutilized to determine a relevancy score, and the relevancy score can beutilized to scale an auction result so that bids related toadvertisements that are more relevant to the identified context arescaled higher than bids related to less relevant advertisements.

In an implementation, the scheduler engine 124 and/or the candidateengine 126 can enforce advertisement restrictions. For example, thescheduler engine 124 and/or the candidate engine 126 can filter theadvertisements to eliminate unwanted advertisements, e.g., frequencycapping can be performed to limit the scheduling of certainadvertisements based on an amount of time since the advertisement waslast aired; competitive restrictions can be applied so that oneadvertisement cannot be placed near another advertisement of acompetitor, etc.

The reporting engine 128 can, for example, receive televisionadvertisement report data from the provider 160 and determine whetherthe selected television advertisement aired based on the televisionadvertisement report data. For example, an advertisement may not air dueto a programming irregularity, e.g., a sporting event going beyond ascheduled broadcast, an interruption to scheduled programming due tobreaking news, etc. In an implementation, the reporting engine 128 canprocess reporting logs, e.g., set top box logs, from viewing devices 164to determine advertisement impressions and/or false positiveimpressions.

The accounting engine 130 can, for example, receive the impression datafrom the reporting engine 128 and generate accounting data foradvertisers. In one implementation, the accounting data can detail feesowed to the television advertising system 100. The fees can, forexample, be based on a cost per action parameter associated with anadvertisement. For example, if an advertiser has specified $10.00 as amaximum cost per thousand impressions for a television commercial, andthe reporting data indicates that 420,000 reliable impressions weregenerated from airing the advertisement, then the advertiser may bebilled for $4,200.

In another implementation, the accounting data can detail fees owed tothe television advertising system 100 and the publisher pursuant to arevenue sharing agreement. An example revenue sharing agreement caninclude a percentage split between the operator of the televisionadvertisement system 100 and the television provider 160. For example,the operator of the television advertising system may receive 20% of thefee, and the operator of the publisher 160 may receive the remaining 80%of the fee. Another example revenue sharing agreement can include afirst fee up to a maximum cap going to the operator of the televisionadvertisement system, and the remaining fee going to the operator of thetelevision provider 160. For example, the operator of the televisionadvertising system may receive the first $500 of the fee, and theoperator of the publisher 160 may receive the remainder of the fee.Other revenue sharing agreements can also be used.

The advertisement front end engine 120, advertisement distributionengine 122, scheduler engine 124, candidate engine 126, reporting engine128, and accounting engine 130 can be implemented separately or incombination. For example, in one implementation, the scheduler engine124 and the candidate engine 126 can be integrated as a single auctionengine 132 on a computing device. Other combinations and/orsubcombinations can also be used.

The system 100 of FIG. 1 can also facilitate the serving of other typesof advertisement availabilities. For example, in addition to servingadvertisement availabilities related to advertisement spots, timesslots, and pods, advertisement availabilities that are dynamic, e.g.,availabilities that are decided in real time, can also be served. Anexample dynamic availability can include the canceling of a scheduledadvertisement, either at the request of the advertiser or automatically,such as when the advertiser's budget is depleted; or in the event of aprogramming delay, e.g., a programming delay for a live event, etc.

FIG. 2 is a block diagram of an example television advertisement frontend system 200. The television advertisement front end system 200 can,for example, be implemented in the advertisement system 100 of FIG. 1.

The advertisement front end system 200 can facilitate the provisioningof advertisement data and television advertisements from the advertiser140 or an agent of the advertiser, and can facilitate associating theadvertisement data with the television advertisements. In oneimplementation, the advertisement front end system 200 can include aweb-based front end interface 202 and an advertisement upload server 204through which the advertiser 140 can upload television advertisementsand associated campaign data associated with the advertisements, e.g.,advertising budgets, targeting data, such as demographics and air times,product and/or service description data, such as verticalclassifications, price ranges, subject matter, etc. FIGS. 5-9 provideexample front end user interface environments.

In one implementation, the advertisement upload server 204 can receivedigital representations of the advertisements, e.g., video files, audiofiles, and text data files, that define the advertisements, e.g. videoadvertisements, including commercials, banners, and logo overlays; audioadvertisements, and text-based advertisements. In anotherimplementation, the advertisements can be provided to the advertisingfront end system 200 in either digital or analog form, e.g., videotapes, DVDs, etc., for processing for storage into the advertisementdata store 102.

The advertisements stored in the advertisement data store 102 mayrequire transcoding into one or more different presentation formats. Forexample, an advertisement may be provided in high definition and in afirst aspect ratio; the advertisement may thus be transcoded to conformto another video standard, such as NTSC or PAL. The transcodedadvertisements can be accessed by a video streamer 212 and provided toprovider 160 for local storage. In one implementation, the provideragent 162 can poll the advertisement front end system 200 periodically,e.g., daily or weekly, to request any new advertisements that have beenuploaded and processed by the advertisement front end system 200.Alternatively, the provider agent 162 can request now advertisementsafter being unable to locate an advertisement locally, or if theprovider 160 indicates that an advertisement cannot be located in alocal data store.

In one implementation, newly added advertisements can be designated aspending approval, and an approval engine 208 can be configured toidentify television advertisements pending approval by a televisionprovider and store the pending advertisements, or links to the pendingadvertisements, in an approval bin 210. The approval engine can receivetelevision provider 160 approval data for each television advertisementpending approval and approve or disapprove the television advertisementbased on the television provider approval data. Approved advertisementscan thereafter be downloaded or otherwise accessed by the provider 160;conversely, disapproved advertisements will not be provided to theprovider 160. Accordingly, only approved advertisements will air onbroadcast signals generated by the provider 160.

In one implementation, advertisements awaiting approval can beautomatically approved after an expiration of a time period, e.g., 72hours. In another implementation, advertisements awaiting approval canbe automatically disapproved after an expiration of the time period.

In another implementation, the approval engine can receive front endsystem 200 approval data for each television advertisement pendingapproval and approve or disapprove the television advertisement based onthe front end system 200 approval data. For example, an operator of thefront end system 200 may enforce various polices for advertisements,e.g., quality requirements, subject matter, etc.

In one implementation, the approval engine 208 can include an automatedapproval engine 209 that is configured to store approval criteria foreach presentation format and evaluate a television advertisement in apresentation format against the corresponding approval criteria. Basedon the evaluation, the automated approval engine 209 can automaticallyapprove or disapprove the advertisement. For example, approval criteriabased on color balance, sound balance, etc. can be utilized toautomatically approve a transcoded advertisement. The automaticallyapproved advertisements or access to the automatically approvedadvertisements can thereafter be provided to the approval bin 210.

After the advertisements are downloaded to the provider 160, or afterthe provider is otherwise provided access to the advertisements so thatthe advertisements can be aired by the provider, the provider agent 162can provide an acknowledgment signal to the advertisement front endsystem 200. The acknowledgement signal can, for example, specify thatthe publisher has received an advertisement or otherwise has access tothe advertisement for airing. The acknowledgement signal can identifythe publisher and be stored in the advertisement parameter data store104 so that each advertisement can be associated with a correspondinglist of publishers 160 that can air the advertisement.

In one implementation, a network interface 214 can be utilized toprovide access to the advertisements stored in the advertisement datastore 104. For example, the network interface 214 can include a searchengine interface and can serve the advertisements over a network, suchas the Internet, in response to search queries that are relevant to theadvertisement. In an implementation, the advertiser 140 can specifywhether an advertisement that can be aired by the publisher can likewisebe served over a network through the network interface 214.

FIG. 3 is a block diagram of an example television advertisementdistribution system 300. The advertisement distribution system 300 can,for example, be implemented in the advertisement system 100 of FIG. 1.

The advertisement distribution system 300 facilitates the storing ofadvertisements on a local data store, e.g., local store 166, associatedwith the television advertiser 160. The storage of the advertisement ata local store 166 can, for example, facilitate real-time or nearreal-time auctioning and scheduling of advertisements, e.g., auctioningand scheduling advertisements for available time slots or spots onlyhours or even minutes before the occurrence of the time slot.

In one implementation, the provider agent 162 can communicate with theadvertisement distribution engine 122 to determine whetheradvertisements are available for storage on the local store 166. In oneimplementation, the provider agent 162 can poll the advertisementdistribution engine 122 periodically, e.g., daily, weekly, etc. Inanother implementation, the advertisement distribution engine 122 cansend a notification to the provider agent 162 when an advertisement isavailable for download, e.g., in response to the provider 160 approvingone or more advertisements.

If advertisements are available for download, the advertisementdistribution engine 122 can direct the provider agent 162 and a videostreamer, e.g., the video steamer 212, to establish a communicationsession for downloading the advertisements from the advertisement store102 to the local store 166. Additional data can also be downloaded,e.g., an advertisement identifier, or other advertisement parameters,e.g., stored in the advertisement parameter store 104. Upon a successfulcompletion of the download, the provider agent 162 can send anacknowledgement signal to the advertisement distribution engine 122. Theacknowledgment signal can, for example, be utilized to associate anadvertisement with a television provider location, and to indicate thatthe television advertisement is stored in a local store 166 at the locusof the provider 160.

In one implementation, the advertisement distribution engine 122 canevaluate advertisement parameters stored in the advertisement parameterstore 104 to determine to which providers 160 the advertisements shouldbe distributed. For example, if the advertisement parameters specifythat an advertisement is related to a California marketing campaign, theadvertisement will only be distributed to providers 160 that service theCalifornia market.

In one implementation, the advertisement can be transcoded by theadvertisement system 100 into a presentation format specified by theprovider 160. In another implementation, the provider 160 can receivethe advertisement in a standard format, e.g., and MPEG format, andtranscode the advertisement into a suitable presentation format.

In another implementation, the advertisements can be streamed from thetelevision advertising system 100 to the provider 162 in near-real timeor during air time. Accordingly, the advertisements need not be storedin a local data store 166.

FIG. 4 is a block diagram of an example television advertisementscheduling and reporting system 400. The television advertisementscheduling and reporting system 400 can, for example, be implemented inthe advertisement system 100 of FIG. 1.

In one implementation, the provider agent 162 can receive advertisementrequests or advertisement availabilities in the form of an availabilityschedule 402. The availability schedule 402 can include a list ofadvertisement availabilities, e.g., time slots, corresponding contexts,e.g., television programs, the advertisement availability type, e.g., asingle spot or a pod of several spots; and other data, such asgeographic data, provider identification data, network data, etc.

The availability schedule 402 can, for example, be providedperiodically, e.g., on a weekly basis for a coming week; or on a dailybasis, or at near real-time or in real time. The provider agent 162 can,for example, communication with a provider interface 170, such as an APIfor a data server managed by the provider 160. In anotherimplementation, the provider interface 170 can be implemented in theprovider agent 162.

The provider agent 162 can provide the availability schedule 402 to thescheduling engine 124 of the advertisement scheduling and reportingsystem 400. The scheduling engine 124 can, for example, communicate withthe candidate engine 126 to identify candidate television advertisementdata associated with advertisements that are eligible to fill theadvertisement spots that are specified in the availability schedule 402.The candidate engine 126 can implement various targeting and/orfiltering rules as described with respect to FIG. 1 above.

The scheduling engine 124 can select one or more televisionadvertisements to air during the television advertisement availabilitydefined in the availability schedule 402. The selection can be based onthe availability schedule 402, e.g., the time slots and associatedcontext, and the candidate television advertisement data. The contextcan be utilized to determine a relevancy score, and the relevancy scorecan be utilized to scale an auction result so that bids related toadvertisements that are more relevant to the identified context arescaled higher than bids related to less relevant advertisements.

The scheduling engine 124 can utilize a Vickrey-style auction based on acost per action, e.g., a cost per 1000 impressions, or a cost pernetwork ($5.00 on network M, $6.00 on network Y), etc. multiplied by aquality score, e.g., a historical impression rate associated with theadvertisement, such as a number of viewers that are determined to haveviewed the advertisement divided by the total number of viewers thatreceived the advertisement. Other factors that can be used to determinethe quality score can be derived from the performance data stored inperformance data store 108, e.g., the performance of a particularadvertisement during particular programming, the probability thatviewers will tune to another channel during an advertisement; theprobability that viewers will fast-forward through the advertisement;etc.

For example, assume the candidate engine 126 identifies threeadvertisements suitable for a particular advertisement spot. Thescheduling engine 124 may determine an auction rank of theadvertisements by multiplying the maximum cost per action for theadvertisements by the quality score of the advertisements. Toillustrate, suppose the quality score (QS) of advertisements A, B, and Care “2,” “3,” and “1.2,” respectively. The rank of advertisements A, B,and C can be determined as follows:

Rank=QS×maximum cost per action=2.0×$5.00=10.00  A

Rank=QS×maximum cost per action=3.0×$7.50=22.50  B

Rank=QS×maximum cost per action=1.2×$10.00=12.00  C

The advertisers can thus be ranked as follows:

1. B

2. C

3. A

Accordingly, the advertisement B would be selected and displayed duringthe advertisement spot. In one implementation, the actual cost an ownerof the advertisement B will pay per thousand impressions can bedetermined by the subsequent advertisement rank (C) divided by the scoreof the advertisement B, e.g., 12/3=$4.00. Other auction processes canalso be used.

In another implementation, for a set of advertisement spots, e.g., a podof several 30-second advertisement spots, each spot can be auctionedseparately. In another implementation, an entire pod can be auctioned,and the highest ranked advertisements can be selected for showing duringthe pod. For example, if the auction illustrated above foradvertisements A, B and C was conducted for a pod of two advertisementspots, commercials B and C would be selected.

In one implementation, an impression rate can be set to an initialdefault value, e.g., a rate equal to an aggregate impression rate foradvertisements in a related demographic or targeting area, and canthereafter be modified based on historical performance.

In an implementation, the scheduler engine 124 and/or the candidateengine 126 can enforce advertisement restrictions. For example, thescheduler engine 124 and/or the candidate engine 126 can filter theadvertisements to eliminate unwanted advertisements, e.g., competitiverestrictions can be applied so that one advertisement cannot be placednear another advertisement of a competitor. For example, a televisionadvertisement availability window for an advertisement spot can begenerated. The advertisement availability window can be time based,e.g., five minutes, or can be advertisement based, e.g., threeadvertisement spots prior to the advertisement spots and threeadvertisement spot subsequent to the advertisement spot. Advertisementsthat have associated competitive restrictions that are exclusive of thecompetitive restrictions of the television advertisements that areselected to air during the television advertisement availability windowcan thus be precluded from selection for that availability window. Forexample, if company A and company B are direct competitors for the sameproduct, and an advertisement slot is available for auction, anadvertisement for company B may only eligible to auction if anadvertisement for company A has not or will not air during theassociated advertisement availability window e.g., within apredetermined number of advertisement slots or predetermined amount oftime.

Frequency capping can be performed to limit the scheduling of certainadvertisements based on an amount of time since the advertisement waslast aired. For example, frequency values associated with the televisionadvertisements can be recorded, e.g., the rate of showings of theadvertisements per hour. If a current frequency value of anadvertisement exceeds a repetition threshold, the advertisement may beprecluded from being shown during advertisement availability.

Likewise, geographic data can be used to filter local advertisements.For example, the availability schedule may define an advertisementavailability in San Francisco, Calif. Accordingly, targeted localadvertisements that are not targeted to San Francisco, e.g., a local cardealership in Los Angeles, Calif., may be precluded from being shownduring an advertisement availability for the locality of San Francisco.

Once the advertisements are selected for an advertisement availability,data related to the selected advertisements and intended display times,e.g. advertisement identifiers and corresponding time slots that thecorresponding advertisements are to be shown, can be provided to theprovider 160 as an advertisement schedule 404. The advertisementschedule 404 can be accessed by an inserter 172 and a modulator 174 thatare utilized to insert advertisements stored in the local store 166 intoa broadcast stream 176.

A verification report 406 that includes data indicating whether theadvertisement aired can be provided to the provider agent 162. In oneimplementation, the verification report 406 includes data that indicateswhether the advertisement aired, the air time of the advertisement, andthe channel on which the advertisement aired. The provider agent 162can, in turn, transmit the verification report 406 to the televisionadvertisement scheduling and reporting system 400 for processing by thereporting engine 128 and storing in the accounting data 112. Dependingon contractual obligations, e.g., whether the advertisers are billedaccording to impressions or are charged a flat fee, the accountingengine 130 may then charge any fees due to the corresponding advertiseraccount.

In another implementation, data related to actual viewings, e.g.,impression data 408, such as set to box log files and reporting records,can be provided to the provider 160. The impression data 408 can, inturn, be provided to the provider agent 162 as an impression data report410. The impression data report 410 can be provided to the reportingengine 128 for processing to determine an impression value related tothe actual and/or estimate of the number of impressions. The impressionvalue can be used by the accounting engine 130 to charge any fees due tothe corresponding advertiser account.

In one implementation, the impression data 408 can be defined by viewingdevice 164 logs, e.g., set top box activity data, such as reportingrecords. For example, user activity, including channel changes andtimestamps, can be recorded and provided to the provider 160periodically, e.g., daily or weekly, in the form of logs. The logs can,for example, be processed by the reporting engine 128 so that thetimestamps are correlated to the times of advertisement insertions. Inone implementation, each viewing device 164 that was tuned to abroadcast stream during a time at which an advertisement was inserted iscounted as an impression. Additionally, other impression data can alsobe determined, such as tune-in and tune-away times for partial views ofan advertisement. As the log data can effectively measure impressionsfor tuned televisions, multiple impressions can be generated perhousehold.

In another implementation, a caching layer 420 can be utilized to cachedata related to advertisement selection and processing of advertisementrequests. The caching layer 420 can, for example, be utilized tooptimize performance of the auctioning process.

In one implementation, the provider agent 162 can anonymize data relatedto particular viewing devices 164 and account information before thedata are received by the television advertising scheduling and reportingsystem 400. Each viewing device 164 can be represented as an anonymousentity, and account information can be associated with locationinformation that has no more granularity than a zip code.

In another implementation, the impression data 408 can be defined bystatistical measurements, e.g., by local and/or regional sampling andextrapolation to a viewership estimate, and can be provided by a thirdparty. For example, Nielsen ratings data can be used to determine aviewership estimate and corresponding impression estimate.

In another implementation, advertisements can be targeted tovideo-on-demand users, digital video recorder users, and the like.Accordingly, the advertisement scheduling data 404 can include real timeor near real time requests.

FIG. 5 is an environment for an example log processing system. A logprocessing engine 502 can receive logs, such as reporting records, fromviewing devices 164, e.g., set to boxes, digital video recorders, etc.,and process the logs to determine the audience of particularadvertisements and infer information about the quality of theadvertisements, e.g. performance of advertisements as measured byimpressions.

The viewing devices 164 can, for example, provide log files that recordthe key presses and channel tunes that the viewing devices 164 havereceived over a time period, e.g., typically one week. The log data canalso include reporting data, e.g., reporting records, that identifieschannel tunes, e.g., a channel identifier to which the viewing devicewas tuned, and channel tune times, e.g., the times that the viewingdevice was tuned to a channel. However, due to processing delays, thelog files of the viewing devices 164 may not identify the same timeindexes for a given airtime. For example, in a broadcast system, ananalog splicer 510 that inserts video frames into a broadcast may have afirst time delay 530. Likewise, a second time delay 540 may be inherentin a broadcast system having an encoder 512, a statistical time divisionmulitplexor (statmux) 514, a transmitting satellite communication device516, a satellite 518, a receiving satellite communication device 520,and a viewing device 164. The time delay 530 and 540 can, for example,be modeled based on particular hardware manufactures. Typically thestatmux 514 inserts a terrestrial dynamical timestamp (TDT), and thusthe time logs on the viewing devices 164 will vary by the satellitetransmission times.

Other delays can also be identified. For example, advertisementsinserted at different locations, e.g., at a head end in New York and ata head end in Los Angeles; or at a root feed in New York and at a localre-broadcaster, can be identified. For example, a first delay time maybe identified for a nationwide broadcast of a commercial at an eastcoast time slot of 20:37:00, and a second delay time may be identifiedfor the nationwide broadcast of the commercial at a west coast time slotof 20:37:00 that same evening.

In some implementations, the tune acquisition time for the televisionprocessing device, e.g., a set top box, can also be modeled. The tuneacquisition time determines how long after a tune is reported before atelevision processing device can actually start rendering the content,and can be device-dependent and/or stream-dependent. For example, adevice-dependent parameter may include the average time or longest timenecessary for a device to lock to a particular frequency; astream-dependent parameter may include the average time or the longesttime for stream to be decoded so that a television can begin displayingcontent. Theses parameters can, for example, be dependent on theencoding properties of the stream and on the television processingdevice model. For example, a first brand of set top boxes may have anaverage tune time acquisition of 550 milliseconds; a second brand of settop boxes may have an average tune time acquisition of 300 milliseconds,etc.

Once the logs for the viewing devices are received, e.g., such as theimpression data 408 received in FIG. 4, the log processing engine 502adjusts the time stamps to take into account the various delays in theservice, for example, between splicing and display. The resultingnormalized log data 504 are analyzed to determine the quality of theadvertisements, e.g., what percentage of people view the advertisementto the end, what is the median tune-out time, average view length etc.For example, the log processing engine 502 may determine that anadvertisement was viewed if, at a time index corresponding to thecompletion of the advertisement, the channel was changed, thusindicating that the viewer watched the advertisement before changing thechannel. Conversely, the log processing engine 502 may determine that anadvertisement was not viewed if, at a time index corresponding to thebeginning of the advertisement, the channel was changed, thus indicatingthat the viewer changed the channel when the advertisement was aired.

Additionally, the log processing engine 502 may detect “falsepositives,” e.g., indications that an advertisement was viewed when, infact, it is highly likely that the advertisement was not viewed. Forexample, if the log data 504 for an anonymized log indicates that aviewer changed channels often until 10:14 PM, and then did not changethe channel for the remainder of the evening, the log processing engine502 may determine that no advertisements were watched by the viewerafter 10:30 PM, as the viewer may have fallen asleep or stopped watchingthe television. In one implementation, the log processing engine 502 mayapply a timeout period of one hour for viewing device 164 events. If aviewing device 164 event does not occur during the time out period, thenit is likely that a viewer is no longer watching the television 524.Other false positive detection algorithms can also be used, such as thedetection of automatically generated channel tune events that can beindicative of channel tunes generated by video recording devices;variable dwell times based on networks and local time of day; channelchange behavior on an anonymized device basis; etc.

An example process that can be implemented in the log processing engine502 can be as follows. Log files (e.g., set top box log files, digitalvideo recorder files, etc.) are copied from a provider, such as theprovider 160, to a local file system accessible by the log processingengine 502. An adjustment process identifies the viewing device 164events and adjusts the timestamps to take into account the variousdelays in the service, e.g., between splicing and display. Falsepositive detection processing can account for false positives byanalyzing the event stream for each viewing device. The viewing deviceevents that are reliable events are utilized to identify advertisementviewings. An impressions processor utilizes the reliable events andinsertion records identifying when advertisements were inserted onparticular channels to determine viewership impressions. The number ofreporting set top boxes can be utilized to extrapolate (or project) theviewership of a particular advertisement if less than all the viewingdevice 164 logs are received. Other metrics can also be calculated,e.g., median tune-out time, percentage of viewers viewing anadvertisement to an end, average viewing time for each advertisement,etc.

FIG. 6A is an example implementation of a log processing system 600 in atelevision advertising system. The log processing system 600 can, forexample, be implemented in the advertisement system 100 of FIG. 1 or inthe log processing system of FIG. 5.

In an implementation, impression data 408, e.g., reporting data, set toplogs, or other data that describes television processing deviceactivity, can be provided to a viewership collector engine 602. Theviewership collector engine 602 can, for example, be implemented in theprovider agent 162, or can be software and/or hardware provided by theprovider 160. The impression data 408 can identify channel tunes andcorresponding tune times for each viewing device, and can also includeother data, such as key press logs, etc.

An anonymization engine 604 can, for example, be utilized to remove anypersonal identifiable information, and reduce granularity to aparticular region, e.g., a zip code, or city, etc. An accounting engine604 can provide anonymized account data, e.g., a non-identifiableaccount profile associated with each set of log data from each viewingdevice 164. Example anonymized account data can include a subscriberpackage detail, e.g., premium provider package, basic provider package,etc.; a list of authorized channels; viewer device 164 population data;channel map data, etc.

The anonymized data can be stored as a raw data 409. Example formats forthe raw data include comma separated value/pipe-delimited data files, orsome other data file of a particular format. The raw data file 409 can,for example, be converted into a formatted data file, such as records inan impression data report 410, and provided to an impression receiverengine 620. Format conversion can, for example, be implemented at theprovider 160, e.g., by the provider agent 162, or can be implemented atthe location of the log processing system 600.

An impression receiver engine 620 can, for example, receive thereporting data from the television provider 160, e.g., the impressiondata report 410, and any other data provided by the television provider160, e.g., programming schedules, etc. The reporting data can includefor example, records that define channel identifiers, associated tuningevents and associated tuning times reported by television processingdevice, e.g., viewer devices 164. Other data can also be provided to theimpression receiver engine 620, such as television provider 160 dataincluding insertion times and intended display times of televisionadvertisements.

The received data 410 can, for example, be stored in a data store 610.In one implementation, the data store 610 can include one or more of theadvertisement data store 102, advertisement parameter store 104, logdata store 106, performance data store 108, out of band data store 110,and the accounting data store 112.

The impression processor engine 630 can receive the reporting recordsfrom the impression receiver engine 620 and execute a normalization andfiltering engine 632 to adjust the associated tuning times fortelevision processing delays and to identify reliable reporting recordsand false-positive reporting records.

Normalization can, for example, adjust the associated tuning times tocompensate for television processing latency, e.g., signal delivery andtuning latency. Given a raw reporting record from a provider and variousmetadata about the provider's broadcasting schedule and processingequipment, the impression processor engine 630 resolves latency issuesbetween an insertion time, an intended display time, and/or an actualbroadcast time. In one implementation, the impression processor engine630 can, for example, also match the channel identifiers of tuningevents reported by the viewer devices 164 with provider programming datato normalize to a globally unique stream of identifiers. For example, aninput to the impression processor engine 630 may be a reporting recorddefining the following channel tune and tune times for a particularviewing device:

V_ID00001111:Channel=123.Start=4:05:00pm.end=4:06:00pm

where V_ID00001111 is an anonymized viewing device 164 identifier,Channel is a channel identifier, Start is a first tune time indicatingwhen the viewing device 164 tuned to the channel identified by thechannel identifier, and End is a second tune time indicating when theviewing device 164 tuned out of the channel identified by the channelidentifier. The impression processor engine 630 can generate thefollowing example normalized reporting record:

V_ID00001111:OperatorID=1243.HeadendID=22.InsertionZone=243.InsertionID=53432934.Channel=123.Start=4:04:52pm.End=4:05:52pm

where the additional fields OperatorID, HeadendID, and InsertionZone areidentifiers related to a particular operator 160 and correspondinggeographic location, and the InsertionID is an advertisement identifier.The corresponding tune times in the normalized reporting record canlikewise be adjusted to compensate for television processing latency.

In an implementation, the tune times can be adjusted according to afixed delay and a variable delay. The fixed delay can be based ontelevision provider encoder hardware and software, or other systemimplementations having a known delay. The variable delay can be based ontelevision provider equipment that may have variable delays, e.g.,analog mixers, encoding changes, etc.

Different latency delays can be determined. For example, the latencydelay can be defined as the time between an insertion time of atelevision advertisement at the television provider and an actualdisplay time of the television advertisement at a viewing device. Inanother implementation, the latency delay can be defined as the timebetween an intended display time of a television advertisement and anactual display time of the television advertisement at a viewing device.Other latency measurements can also be used.

In one implementation, processing latency adjustments can includecorresponding error estimates if an adjustment uncertainty exists. Inone implementation, the impression processor engine 630 can alsogenerate original reporting records from the normalized reportingrecords.

The impression process engine 630 can, for example, also filter thenormalized reporting records according to one or more filtering rules toidentify reliable normalized reporting records and false-positivenormalized reporting records. For example, the data related to theintended display times, insertion times, and actual display times can becompared to one or more filtering rules, such as dwell time filtering,idle filtering, and authorization filtering.

Dwell time filtering can, for example, determine if corresponding tunetimes for a channel tune define a duration exceeding a minimum dwelltime threshold, and upon a positive determination, associate thecorresponding tune times for the channel tune as a reliable duration.For example, if a user is channel surfing, the user may generate manytuning events that do not correspond to having genuinely paid attentionto the channel, and therefore not paid attention to an advertisementthat may have aired. If the dwell time of any viewing is less than acertain dwell time threshold, e.g., two seconds, five seconds, etc., thechannel tunes can be considered a channel surfing event in which theuser did not pay attention to the advertisement, and thus an impressionwould not be generated.

Other log data, such as key presses, can also be used to identifychannel surfing and define dwell times. For example, the entering of achannel number manually may be a weak indicator of channel surfing,which the repetitive actuation of an up or down channel key may be astrong indicator of channel surfing. In one implementation, a dwell timethreshold can be adjusted depending on a user behavior. For example, adwell time of 15 seconds may be imposed in a non-channel surfingcontext; however, the dwell time can be reduced to 10 seconds during achannel surfing context.

In some implementations, log data can be processed to determine ifmultiple channel tunes occurred simultaneously, and impressions and/orcosts associated with one or each of the simultaneous channel tunes canbe adjusted or ignored. For example, log data may indicate that thetelevision processing device was tuned to two channels simultaneously,e.g., a “picture-in-picture” operation. In these implementations, thereporting records for one (e.g., the main channel tune or the“picture-in-picture” channel tune) can be identified as false-positivereporting records.

Idle filtering can, for example, determine if corresponding tune timesfor a channel tune define a duration that exceeds a maximum dwell timethreshold, and upon a positive determination, associate thecorresponding tune times for the channel tune as a false-positiveduration. For example, common usage patterns are turning off atelevision while leaving viewing device 164 on, leaving a room for anextended time while the television remains on; and falling asleep duringa television program. Consequently, a record of viewer events thatindicates a user watching the same channel for an extended time withoutchanging the channel, e.g., four hours, may generate many false positiveimpressions, as the viewer may have turned off the television, left theroom, or fallen asleep.

In some implementation, idle filtering parameters can, for example, beadjusted based on a corresponding programming event. For example, amaximum dwell time threshold can be set in substantial proportion to aprogramming event, e.g., a 30 minute programming event can have amaximum dwell time of 30 minutes; a three hour sporting event can have amaximum dwell time of three hours, etc. In another implementation, themaximum dwell time threshold can be adjusted based on an aggregatelength of programming events. For example, four 30 minute programmingevents may have a corresponding maximum dwell time of 45 minutes. Thus,if during a broadcast of four half-hour sitcoms the viewing device 164does not indicate a channel tune change, impressions will not begenerated for viewings beyond the initial 45 minute dwell time.

In another implementation, idle filtering can be adjusted based onviewer behavior during previous programming. For example, idle historytimes can be based on viewer behavior collected over time, e.g., an idlefilter time for a first program or program content (e.g., televisionnews talk programs) may be 15 minutes; while an idle history time for asecond program or program content (e.g., a three hour long program) maybe two hours based on historical viewer behavior.

Authorization filtering, can, for example, determine if a channel tuneis an authorized channel tune, e.g., a channel to which a user issubscribed. If the channel tune is not an authorized channel tune, thenauthorization filtering can preclude association of the correspondingtune times for the channel tune as a reliable duration. For example, ifa user has not subscribed to the channel tuned, it is unlikely the usercould have viewed the advertisement placed on that channel.

An impression analyzer engine 640 can be configured to receive thenormalized reporting records from the impression processor engine 630.In an implementation, the impression analyzer engine 640 can implement acorrelation engine 642 to correlate the reliable normalized reportingrecords with advertisement insertion records to determine reportedimpressions for corresponding television advertisements. For example,the impression analyzer engine 640 can correlate reliable durations withthe advertisement schedule 404 defining advertisement insertion recordspreviously provided to the television provider 160. Accordingly,reliable normalized reporting records that include channel tunes andtune times that indicate a viewing device 164 displayed theadvertisement during a reliable duration can correspond to an impressionfor the advertisement.

Typically, all viewing devices 164 will not have reported for a giventime period, e.g., the entire corpus of viewing devices 164 can providelog data at different times. Accordingly, the true total number ofactual impressions may not be exactly measured until all viewing device164 have reported for a given time period. Thus, at any one time it islikely only a subset of all viewing devices 164 have provided log datato the log processing system 600. Accordingly, an estimation engine 644can, for example, estimate the projected impressions for thecorresponding television advertisements based on the reportedimpressions.

In one implementation, the estimation engine 644 of the impressionanalyzer engine 640 can determine a percentage of a maximum number ofreporting records received. For example, if a television provider has100,000 subscriber viewing devices, and 50% of the viewing devices haveprovided log data for a Monday afternoon time slot, then the percentageof a maximum number of reporting records received is 50%.

The percentage of reporting records received can define a sampleweighting, and the sample weighting can be adjusted for a sample bias.For example, the estimation engine 644 can thus estimate the projectedimpressions for the corresponding television advertisements based onpercentage of the maximum number of reporting records and the reportedimpressions for the corresponding television advertisements. In oneimplementation, the estimate can, for example, be a linearextrapolation. For example, if the reported 50% of all availablereporting records received yield 2,100 impressions for an advertisementaired during a particular advertisement availability, then the projectedimpressions can be 4,200.

As reporting records are likely to be received from the provider 160over a period of several days, or even weeks, the impression processorengine 630 and the impression analyzer engine 640 can, for example,iteratively process newly arrived reporting records. In oneimplementation, the impression processor engine 630 and the impressionanalyzer engine 640 are configured to iteratively update the projectedimpressions during an impression time window. The iterative updates can,for example, be based on subsequently received reporting records fromthe television provider 160 during the impression time window. In oneimplementation, the impression time window can be approximately oneweek.

After each iteration, an error value related to the projectedimpressions can be calculated. Typically, as the number of log recordsprocessed for a given advertisement availability increases, the errorvalue decreases. In an implementation, an advertiser's account may onlybe charged upon the occurrence of the error value decreasing below athreshold value, or upon the expiration of the impression time window.

A reporting recorder engine 650 can, for example, store the impressiondata related to advertisements in the data store 610. For example, thereporting recorder engine 650 an update performance data related to anadvertisement, which can, in turn, affect the quality score of theadvertisement and future selections of the advertisement. In oneimplementation, the impression recorder engine 650 can also implement abilling process 652, e.g., a call to the accounting engine 130, whichcan generate corresponding advertiser billings.

In another implementation, false-positive detection can be identifiedfrom original reporting records, i.e., non-normalized reporting records.For example, an automated tuning detection engine 660 can receivereporting records from the impression receiver engine 620, identifyreported channel tunes and corresponding tune times of the televisionprocessing devices from the reporting records, and identifyautomatically generated channel tunes based on the corresponding tunetimes.

For example, reporting records can be processed by the automated tuningdetection engine 660 to distinguish between viewer-triggered channeltunes and automatically generated channel tunes. The automaticallygenerated channel tunes can, for example, be indicative of a recordingdevice tuning to a channel to record a program. For such channel tunecharacteristics, it is more likely that a viewer is not present to watchthe program during the actual broadcast time. Depending on otherviewer-triggered channel tunes and/or other viewer triggered dataproximate in time in to the identified automatically generated channeltunes, advertisement costs, e.g., costs per action, can be discounted,delayed or even waived with respect to a television processing devicefor which advertisements aired on a channel that was tuned to by anautomated channel tune.

Viewer-triggered channel tunes can be differentiated from automaticallygenerated channel tunes based on viewer reaction times. While bothtuning types tend to cluster together at certain times, e.g., at thestart and end of programming events, tune times for viewer-triggeredchannel tunes are loosely correlated relative to the correlation of tunetimes for automatically generated channel tunes.

Additionally, a period of inactivity at the television processing deviceand/or at a control device associated with the television processingdevice, e.g., a set top box digital video recorder (DVR) remote control,can define a dwell time that can be used to further determine whether aviewer was present to watch a program during the actual broadcast time.For example, if the television processing device does not processviewer-input television processing device commands, e.g., channel tunes,volume adjustments, menu key presses, other inputs indicating a viewerpresence, the likelihood that a viewer was present to watch the programduring the actual broadcast time decreases.

FIGS. 6B-6E illustrate the dwell time plots that indicate channel tunesthat are likely to be automatically generated by a television processingdevice. FIGS. 6B-6D are example dwell time plots indicating dwell timesfor television processing devices over a time period, e.g., one week. Inthe plots of FIGS. 6B-6D, the dwell times measure channel tune outs,e.g., a set top box that remains tuned to a particular channel for 60minutes and is then tuned to another channel would define a dwell timeinterval of 60 minutes.

As illustrated in FIG. 6B, the dwell times tend to decay at anexponential rate. Because, however, many programs are broadcast inincrements of 30 minutes, a relatively large number of channel tunes aredistributed at tune times near the end of each 30 minute increment,creating dwell time clusters. Thus at certain dwell times, such as 30,60, 90 and 120 minutes, the occurrence of dwell times increase. Forexample, near the actual durations of 30, 60, 90 and 120 minutes, thenumber of dwell times can have a Gaussian distribution, or adistribution similar to a Gaussian distribution. Furthermore, manychannel tunes occur at characteristic dwell times, e.g., exactly at30:00, or 31:00, 32:00, etc. These channel tunes are likely to becomposed of automatic channel tunes, as such characteristic times arelikely indicative of users programming a recording device to stoprecording exactly at a program end time, or to extend beyond a programend time by up to several minutes to ensure that the entire program isrecorded. These characteristic dwell time are also indicative of adefault padding time implemented in a recording device.

FIG. 6C is a more detailed dwell time plot for dwell time periods of27-34 minutes at a dwell bin resolution of 4 seconds. The dwell times of30:00, 31:00, 32:00, 33:00 and 34:00 have significant proportionalincreases relative to prior and subsequent dwell times, e.g., dwelltimes of 29:56 and 30:04; 30:56 and 31:04, etc. Discretizing the dwellstimes to a finer dwell time bin size, e.g., 1 second, can furtheremphasize the proportional gain at the characteristic times of 30:00,31:00, 32:00, 33:00 and 34:00. The relatively flat increase in dwelltimes between the times of 30:00 and 31:00 can be attributed toviewer-triggered channel tunes.

FIG. 6D is a more detailed dwell time plot for dwell time periods of42:00-49:00 minutes at a dwell bin resolution of 4 seconds. The dwelltimes remain relatively flat, indicative of viewer-triggered channeltunes. Such a flat distribution is expected over this period, asautomatically generated channel tunes would be less likely to occur atthese dwell times.

FIG. 6E is an example plot of channel tunes during a broadcast timeperiod from the hours of 18:30-22:00. The plot of channel tunesillustrate highly correlated channel tune clusters at characteristictune times, e.g., 18:59, 19:00, 19:01, 19:29, 19:30, 19:31, etc. A linepattern 670 corresponds to channel tunes defined by reporting data for aparticular set top box and the resultant dwell times t1-t6. Thecorresponding channel tunes and tune times related to the line pattern670 are provided in Table 1 below:

TABLE 1 Dwell Period Channel ID Tune In/On Tune Out/Off Dwell Time t1 3318:27:02 19:02:11 35:09 t2 36 19:02:11 19:09:12  7:01 t3 51 19:09:1219:20:34 11:22 t4 11 19:20:34 19:29:00  8:26 t5 124 19:29:00 20:01:0032:00 t6 8 21:29:00 22:01:00 32:00

The channel tunes and tune times listed in Table 1 above and illustratedin FIG. 6E for a particular set top box can be interpreted as evidencingviewer-triggered channel tunes during the dwell time periods t1, t2, t3and t4, as these dwell times are random and correspond to tune timesindependent of characteristic tune times. However, the channel tunesduring the dwell periods t5 and t6 can be interpreted as automaticallygenerated channel tunes, as these channel tunes occur at correspondingchannel tune cluster pairs and define dwell times that have significantproportional increases relative to prior and subsequent dwell times,e.g., 32 minutes.

Other dwell time or tune time characteristics can also be used. Forexample, the dwell time of 32 minutes can be contingent of the observedbehavior of a viewing populace. In some implementations, a defaultbehavior of a television processing device can also be used to identifyautomatically generated channel tunes, e.g., a particular televisionprovider may have set top boxes with DVR capability that by defaultbegin two minutes before a scheduled electronic program guide start timeand extend five minutes beyond a scheduled electronic program guide endtime and.

In one implementation, an advertising cost for an advertisement thataired on the channel defined by the channel tune and during a timeperiod defined by channel tune cluster pairs, e.g., the broadcast timeperiods defined by the dwell times t5 or t6, can be discounted. Thediscount can, for example be adjusted based on a likelihood that aviewer watched the program being recorded during the actual airtime. Thelikelihood can be based on, for example, the proximity in time ofchannel tunes that are unassociated with the automatically generatedchannel tunes. For example, the channel tunes for dwell periods t1-t4are proximate in time to dwell period t5, thus indicating viewerinteractions and therefore a viewer presence at the beginning the dwellperiod t5. Accordingly, only a small discount may be applied to anadvertising cost, or even no discount may be applied.

The channel tunes for dwell periods t1-t4, however, are not proximate intime to the dwell period t6, thus indicating a viewer was not present atthe beginning of the dwell period t6. Accordingly, a full discount maybe applied to an advertising cost, or the advertisement cost can bewaived in its entirety with respect to the particular televisionprocessing device, i.e., the impression is identified as a falsepositive and processed as described above. Other discounts can also beapplied, e.g., a discount can be applied in substantial proportion tothe length of the dwell time.

In some implementations, the automated tuning detection engine 660 canidentify false-positive impressions based on the channel tune clustersat corresponding tune times. Based on the behavior model describe above,the automated tuning detection engine 660 can also identify the channeltune clusters as automatically generated channel tunes. For example, theautomated tuning detection engine 660 can identify dwell time counts fortelevision processing devices from the reporting data to generate thedata of FIG. 6C defining in a dwell time count relative to adjacentdwell time counts exceeds a threshold, e.g., 3 dB, or some otherthreshold measure. If the threshold is exceeded, the channel tunecluster can be identified automatically generated channel tunes. Forexample, the channel tunes at the dwell times of 30:00, 31:00, 32:00,33:00 and 34:00 can be identified as automatically generated channeltunes, while the channel tunes at the times 29:32, 31:07, etc. are notidentified as automatically generated channel tunes.

In some implementations, the automated tuning detection engine 660 candetermine if channel tunes related to a television processing device,e.g., a set top box DVR, correspond to a channel tune cluster pair. Ifthe automated tuning detection engine 660 determines that the channeltunes related to the television processing device correspond to achannel tune cluster pair, then the accounting engine 130 can discountan advertising cost for an advertisement that aired on the channeldefined by the channel tune and during a time period defined by thechannel tune cluster pairs. The discount can, for example, be adjustedin substantial proportion to a length of the time period defined by thechannel tune cluster pair, or can be adjusted in substantially inverseproportion to the proximity in time of unassociated channel tunes to thetime period defined by the channel tune cluster pair. Other advertisingcost adjustment or delayed billing schemes can also be used.

In another implementation, the automated tuning detection engine 660 canidentify false-positive impressions based on identifying channel tunesoccurring at channel tune times that correspond to triggering tunetimes. The triggering tune times can, for example, be tune times thatare often utilized by automated recording devices, such as programmingstart times and end times for television programs, or rounded timevalues, e.g., 18:29:00, 18:30:00, 18:31:00, etc. In someimplementations, different sets of triggering tune times can be comparedto increase the accuracy of identifying false-positives impressions. Forexample, a rounded time value of 18:30:00 can correspond to a programstart time and/or end time, while a rounded time value of 18:21:00 doesnot correspond to a program start or end time. Accordingly, channeltunes occurring at the tune time of 18:30:00 may be determined to beautomatically generated, while channel tunes occurring at the tune timeof 18:21:00 may not be determined to be automatically generated.

In some implementations, the automated tuning detection engine 660 canstore a list of the start and end times of programs defined inelectronic program guide data, and can store data related tocharacteristic padding schemes used by the television processing device.Example padding schemes can include starting a recording operation torecord a program one minute before the program start time if there is norecording preceding that time; stopping three minutes after the programend time if there is no recording scheduled within the three minutesafter the program end time; continuing to record for 30 minutes beyondthe program end time if the program is a sporting event, etc. Theautomated tuning detection engine 660 can analyze reporting records toidentify channel tune times that start at a characteristic time before aprogram start time, and which end at a characteristic time after theprogram end time.

In some implementations, electronic programming guide data can bedelivered out of band as static data, e.g., data related to regularlyscheduled television events. Alternatively, the electronic programmingguide data can be delivered from monitoring the data stream recorded bythe television processing device, e.g., digital video broadcastingin-band delivery of electronic programming guide data. The in-bandelectronic programming guide data can facilitate the processing ofinformation for live sporting events that can have unpredictable endingtimes. For example, a “program over” signal delivery may be delayed fordelivery in-band if a sporting event goes into double overtime.Accordingly, a large number of channel tunes may be automaticallygenerated at an unexpected dwell time or broadcast time, e.g., thesporting event may air from 7:30 PM to 11:43:19, resulting in channeltune clusters at 7:29:00 and at 11:45:19.

In some implementations, the automated tuning detection engine 660 canidentify tune in and tune out channel tunes at tune times that areshared by a larger population of television processing devices thanwould be expected by random chance. If this correlated behavior issufficiently more frequent than a random occurrence, e.g., 10× morecorrelated than channel tunes at proximate tune times, then the channeltunes at the matching tune times can be indicative of an event that hasbeen manually scheduled by individuals independent normal programmingschedules. In some implementations, such identification can beimplemented without electronic programming guide data.

In some implementations, the automated tuning detection engine 660 canidentify channel tune times that start at a characteristic time beforeor after rounded time values e.g., 0, 15, 30 and 45 minutes after thehour, and identify channel tune clusters, should such channel tuneclusters be identified, as manually-programmed triggering tune times. Ifsuch a channel tune cluster exhibits a high correlation relative to thepreceding and subsequent channel tunes, then the channel tune clustercan be identified as automatically generated channel tunes.

Once the automated tuning detection engine 660 identifies reportingrecords that are indicative of automatically generated channel tunes,e.g., channel tunes likely to be triggered by a DVR timer, theaccounting engine 130 can optionally adjust an advertising cost foradvertisements that were likely recorded. In one implementation, theadvertisement cost adjustment can be applied on a per-device basis,e.g., the impression associated with the television processing devicecan be identified as a false-positive and not accounted for.

In another implementation, the accounting engine 130 can apply aprorated billing based on overall data, e.g. if a viewer survey orcorresponding data analysis finds that 40% of advertisements in recordedprograms are skipped during playback, the advertisement cost can bereduced by 40%.

In another implementation, the accounting engine 130 can adjust theadvertising cost in substantial proportion to the length of the dwelltime. For example, an automatically generated dwell time of three hourscan be discounted more than an automatically generated dwell time of 30minutes.

The impression receiver engine 620, the impression processor engine 630,the impression analyzer engine 640, the impression recorder engine 650,and the automated tuning detection engine 660 can be implementedseparately or in combination. For example, in one implementation, theimpression receiver engine 620 can be integrated into the impressionprocessor engine 630 on a computing device. Other combinations and/orsubcombinations can also be used.

In another implementation, the automated tuning detection engine 660 canprocess the distribution of behavior of viewers tuning in just beforeand just after the automatically generate channel tune, and estimate,e.g., interpolate, a value coincidental user tunes, e.g., “background”channel tunes. The coincidental user tunes can be excluded from falsepositive estimates. Table 2 below lists example channel tunes proximatein time to 29:00.

TABLE 2 Time Channel Tunes 28:57 230 28:58 232 28:59 231 29:00 223029:01 231 29:02 229 29:03 230

Based on the distribution, the automated tuning detection engine 660can, for example, estimate that 2,000 channel tunes were automaticallygenerated at the time 29:00, e.g., 2230-230.

As described above, the automated tuning detection engine 660 canprocess reporting records from television processing devices, e.g., settop boxes, DVRs, etc., and identify reporting records that correspond toa signature of an automatically-triggered channel tuning event, e.g.,highly correlated channel tunes or channel tune clusters; channel tuneclusters at points corresponding to the beginning and end of programsappearing in electronic program guides; or channel tune clusterscorresponding to round numbers relative to a time reference. Theseidentified channel tunes can thus be differentiated fromviewer-triggered channel tunes.

FIG. 7 is a flow diagram of an example process 700 for processing logsand determining impressions from the processed logs. The process 700can, for example, be implemented in the television advertising system100 of FIG. 1, or in the log processing system 500 of FIG. 5, or in thelog processing system 600 of FIG. 6A.

Stage 702 receives reporting data, e.g., from a television provider. Forexample, the reporting engine 128 of FIG. 1, the log processing engine502 of FIG. 5, or the impression receiver engine 620 and/or theimpression processor engine 630 of FIG. 6A can receive reporting data,e.g., an impression data report 410, from a provider 160.

Stage 704 identifies channel tunes and corresponding tune times from thereporting data. For example, the reporting engine 128 of FIG. 1, the logprocessing engine 502 of FIG. 5, or the impression receiver engine 620and/or the impression processor engine 630 of FIG. 6A can identifychannel tunes and correspond tune times from the reporting data.

Stage 706 identifies reliable durations based on the identified channeltunes and corresponding tune times. For example, the reporting engine128 of FIG. 1, the log processing engine 502 of FIG. 5, or theimpression receiver engine 620 and/or the impression processor engine630 of FIG. 6A can detect reliable durations by comparing the reportingrecords to evaluation criteria, such as dwell times, idle times, andauthorized channels.

Stage 708 identifies content items including, for example, televisionadvertisements that were aired during the tune times on channelscorresponding to the channel tunes. For example, the reporting engine128 of FIG. 1, the log processing engine 502 of FIG. 5, or theimpression analyzer engine 640 can compare the reliable durations to anadvertisement schedule 404 defining advertisement insertion records toidentify television advertisements that were aired during reliabledurations.

Stage 710 determines an impression value for each identified contentitem, e.g., television advertisement, based on the reliable durations.For example, the reporting engine 128 of FIG. 1, the log processingengine 502 of FIG. 5, or the impression receiver engine 620 and/or theimpression processor engine 630 of FIG. 6A can generate an impressionvalue for each identified television advertisement that aired on achannel tune during a reliable duration for that channel tune.

FIG. 8 is a flow diagram of an example process for adjusting log data tocompensate for broadcast delays. The process 800 can, for example, beimplemented in the television advertising system 100 of FIG. 1, or inthe log processing system 500 of FIG. 5, or in the log processing system600 of FIG. 6A.

Stage 802 identifies a latency delay between an insertion time of acontent item, e.g., a television advertisement, at the televisionprovider and an air time of the content item at a viewing device. Forexample, the reporting engine 128 of FIG. 1, the log processing engine502 of FIG. 5, or the impression receiver engine 620 and/or theimpression processor engine 630 of FIG. 6A can identify a latency delay,such as the time between an insertion time of a television advertisementat the television provider and an actual display time of the televisionadvertisement at a viewing device, or the time between an intendeddisplay time of a television advertisement and an actual display time ofthe television advertisement at a viewing device.

Stage 804 adjusts the tune times to compensate for the latency delay.For example, the reporting engine 128 of FIG. 1, the log processingengine 502 of FIG. 5, or the impression receiver engine 620 and/or theimpression processor engine 630 of FIG. 6A can adjust the tune items inthe reporting records to generate normalized reporting records.

FIG. 9 is a flow diagram of an example process for identifying reliabledurations and false positive durations. The process 900 can, forexample, be implemented in the television advertising system 100 of FIG.1, or in the log processing system 500 of FIG. 5, or in the logprocessing system 600 of FIG. 6A.

Stage 902 determines if corresponding tune times for a channel tunedefine a duration exceeding a maximum dwell time threshold. For example,the reporting engine 128 of FIG. 1, the log processing engine 502 ofFIG. 5, or the impression receiver engine 620 and/or the impressionprocessor engine 630 of FIG. 6A can determine if tune times for aparticular channel tune define a duration exceeding a maximum dwell timethreshold, e.g., one hour.

If stage 902 determines that the corresponding tune times for a channeltune do define a duration exceeding the maximum dwell time threshold,then stage 904 associates the corresponding tune times for the channeltune as a false positive duration. For example, the reporting engine 128of FIG. 1, the log processing engine 502 of FIG. 5, or the impressionreceiver engine 620 and/or the impression processor engine 630 of FIG.6A can associate tune times defining a viewing duration of three hourswith at least two hours of false positive duration, e.g., the last twohours of the three hours would not be utilized to generate impressions.

If, however, stage 902 determines that the corresponding tune times fora channel tune do not define a duration exceeding a maximum dwell timethreshold, then stage 906 associates the corresponding tune times forthe channel tune as a reliable duration. For example, the reportingengine 128 of FIG. 1, the log processing engine 502 of FIG. 5, or theimpression receiver engine 620 and/or the impression processor engine630 of FIG. 6A can associate tune times defining a viewing duration ofthirty minutes as a reliable duration, e.g., the entire thirty minuteduration can be utilized to generate impressions.

FIG. 10 is a flow diagram of another example process for processing logsand determining impressions from the processed logs. The process 1000can, for example, be implemented in the television advertising system100 of FIG. 1, or in the log processing system 500 of FIG. 5, or in thelog processing system 600 of FIG. 6A.

Stage 1002 identifies channels and associated content item slots, e.g.,advertisement time slots, or advertisement overlay availabilities, etc.For example, the reporting engine 128 of FIG. 1, the log processingengine 502 of FIG. 5, or the impression analyzer engine 640 of FIG. 6Acan identify channels and associated advertisement slots from theadvertisement schedule 404.

Stage 1004 identifies corresponding channel tunes and tune times. Forexample, the reporting engine 128 of FIG. 1, the log processing engine502 of FIG. 5, or the impression analyzer engine 640 of FIG. 6A canidentify corresponding channel tunes and tune times from the impressiondata report 410.

Stage 1006 identifies content items, e.g., television advertisements,associated with the identified channels and the content item slots. Forexample, the reporting engine 128 of FIG. 1, the log processing engine502 of FIG. 5, or the impression analyzer engine 640 can correlate theimpression data report 410 with the advertisement schedule 404 toidentify television advertisements associated with the channels andadvertisement slots defined by the tune times and channel tunes.

Stage 1008 identifies an impression for each television advertisementassociated with an identified channel and advertisement time slot thatcorresponds to a reliable duration. For example, the reporting engine128 of FIG. 1, the log processing engine 502 of FIG. 5, or theimpression analyzer engine 640 of FIG. 6A can generate an impressionvalue for each identified television advertisement that aired on achannel tune during a reliable duration for that channel tune.

FIG. 11 is a flow diagram of another example process for processing logsand determining impressions from the processed logs. The process 1100can, for example, be implemented in the television advertising system100 of FIG. 1, or in the log processing system 500 of FIG. 5, or in thelog processing system 600 of FIG. 6A.

Stage 1102 receives reporting records defining channel identifiers,associated tuning events and associated tuning times reported by tuningdevices. For example, the reporting engine 128 of FIG. 1, the logprocessing engine 502 of FIG. 5, or the impression receiver engine 620and/or the impression processor engine 630 of FIG. 6A can receivereporting records from the television provider 160 or from a thirdparty.

Stage 1104 adjusts the associated tuning times to compensate fortelevision processing latency. For example, the reporting engine 128 ofFIG. 1, the log processing engine 502 of FIG. 5, or the impressionreceiver engine 620 and/or the impression processor engine 630 of FIG.6A can adjust the tune times by offsetting the tune times by theidentified latency.

Stage 1106 generates normalized reporting records based on the adjustedassociated tuning times that include television provider identifiers,insertion identifiers, and time stamps. For example, the reportingengine 128 of FIG. 1, the log processing engine 502 of FIG. 5, or theimpression receiver engine 620 and/or the impression processor engine630 of FIG. 6A can generate normalized reporting records that includethe adjusted tune times and, if applicable, other data.

Stage 1108 filters the normalized reporting records to identify falsepositive reporting records. For example, the reporting engine 128 ofFIG. 1, the log processing engine 502 of FIG. 5, or the impressionreceiver engine 620 and/or the impression processor engine 630 of FIG.6A can identify reporting records for which a portion of the tunedurations defined by the channel tunes are determined to be falsepositive durations, e.g., a tune duration of two hours may be determineto have 30 minutes of reliable duration and ninety minutes of falsepositive duration.

Stage 1110 determines impression values for content items, e.g.,television advertisements, based on the false positive reportingrecords. For example, the reporting engine 128 of FIG. 1, the logprocessing engine 502 of FIG. 5, or the impression analyzer engine 640of FIG. 6A can generate impression values from the channel tunedurations determined not to be false positive durations, e.g., reliabledurations.

FIG. 12 is a flow diagram of an example process 1200 for estimating animpression total from log data. The process 1200 can, for example, beimplemented in the television advertising system 100 of FIG. 1, or inthe log processing system 500 of FIG. 5, or in the log processing system600 of FIG. 6A.

Stage 1202 correlates the normalized reporting records withadvertisement insertion records to determine reported impressions forcorresponding television advertisements. For example, the reportingengine 128 of FIG. 1, the log processing engine 502 of FIG. 5, or theimpression analyzer engine 640 of FIG. 6A can correlate normalizedreporting records with the advertisement schedule 404 to determinereported impressions corresponding to a television advertisement.

Stage 1204 estimates projected impressions for the correspondingtelevision advertisements based on the reported impressions and theidentified false positive reporting records. For example, the reportingengine 128 of FIG. 1, the log processing engine 502 of FIG. 5, or theimpression analyzer engine 640 can estimate projected impressions forthe corresponding television advertisements based on the reportedimpressions and the identified false positive reporting records.

FIG. 13 is a flow diagram of an example process 1300 for iterativelyprocessing logs. The process 1300 can, for example, be implemented inthe television advertising system 100 of FIG. 1, or in the logprocessing system 500 of FIG. 5, or in the impression receiver engine620, the impression processor engine 630, the impression analyzer engine640, and the reporting recorder engine 650 of FIG. 6A.

Stage 1302 defines an impression time window. For example, the reportingengine 128 of FIG. 1, the log processing engine 502 of FIG. 5, or theimpression the reporting recorder engine 650 of FIG. 6A may define animpression time window, e.g., a week.

Stage 1304 iteratively process reporting records based on subsequentlyreceived reporting records during the impression time window. Forexample, the reporting engine 128 of FIG. 1, the log processing engine502 of FIG. 5, or the impression receiver engine 620, the impressionprocessor engine 630, the impression analyzer engine 640, and thereporting recorder engine 650 of FIG. 6A may iteratively process thereporting records each time reporting records related to a particularadvertisement availability are received during the impression timewindow.

Stage 1306 generates advertiser billings based on the projectedimpressions after the expiration of impression time window. For example,the reporting engine 128 of FIG. 1, the log processing engine 502 ofFIG. 5, or the reporting recorder engine 650 of FIG. 6A may generatebillings after the expiration of the impression time window.

FIG. 14 is a flow diagram of an example process 1400 for adjustingadvertising costs based on automatically generated channel tune times.The process 1400 can, for example, be implemented in the televisionadvertising system 100 of FIG. 1, or in the impression receiver engine620, the automated tuning detection engine 660, and the reportingrecorder engine 650 of FIG. 6A.

Stage 1402 receives reporting data related to television processingdevices. For example, the reporting engine 128 of FIG. 1, or theautomated tuning detection engine 660 of FIG. 6A can receive reportingdata related to television processing devices, e.g., set top boxmetering records.

Stage 1404 identifies channel tunes and corresponding tune times of thetelevision processing devices from the reporting data. For example, thereporting engine 128 of FIG. 1, or the automated tuning detection engine660 of FIG. 6A can identify channel tunes and corresponding tune timesof the television processing devices from the reporting data.

Stage 1406 identifies automatically generated channel tunes based on thecorresponding tune times. For example, the reporting engine 128 of FIG.1, or the automated tuning detection engine 660 of FIG. 6A can identifyautomatically generated channel tunes based on the corresponding tunetimes. For example, the tune times of channel tunes can be compared tocharacteristic tune times; or can be highly correlated with a channeltune cluster; or can correspond to program broadcast times defined inelectronic programming guide data.

Stage 1408 adjusts advertising costs associated with the presentation ofcontent items, e.g., advertisements, based on the identifiedautomatically generated channel tunes. For example, the accountingengine 130 or the reporting engine 650 can adjust advertising costsassociated with advertisements based on the identified automaticallygenerated channel tunes.

FIG. 15 is a flow diagram of an example process 1500 for identifyingautomatically generated channel tunes. The process 1500 can, forexample, be implemented in the television advertising system 100 of FIG.1, or in the automated tuning detection engine 660 of FIG. 6A.

Stage 1502 identifies channel tune clusters at corresponding tune times.For example, the reporting engine 128 of FIG. 1, or the automated tuningdetection engine 660 of FIG. 6A can identify channel tune clusters atcorresponding tune times.

Stage 1504 identifies the channel tune clusters as the automaticallygenerated channel tunes. For example, the reporting engine 128 of FIG.1, or the automated tuning detection engine 660 of FIG. 6A can identifythe channel tune clusters as the automatically generated channel tunes.

FIG. 16 is a flow diagram of an example process 1600 for adjustingadvertising costs based on identified channel tune clusters. The process1600 can, for example, be implemented in the television advertisingsystem 100 of FIG. 1, or in the automated tuning detection engine 660and the reporting recorder engine 650 of FIG. 6A.

Stage 1602 identifies channel tune cluster pairs. For example, thereporting engine 128 of FIG. 1, or the automated tuning detection engine660 of FIG. 6A can identify channel tune cluster pairs, e.g., channeltune clusters proximate to the time of 20:30:00 and 21:00:00, forexample.

Stage 1604 determines if the identified channel tunes related to atelevision processing device correspond to a channel tune cluster pair.For example, the reporting engine 128 of FIG. 1, or the automated tuningdetection engine 660 of FIG. 6A can determine if the identified channeltunes for a television processing device occurred at a channel tunecluster pair, e.g., a tune-in to a channel at 20:29:00, and a tune-outfrom the channel or turn off of the television processing device at atime of 21:03:00, for example.

If stage 1604 determines that the identified channel tunes related to atelevision processing device correspond to a channel tune cluster pair,then stage 1606 discounts an advertising cost for an advertisement thataired on the channel defined by the channel tune and during a timeperiod defined by the channel tune cluster pairs. For example, theaccounting engine 130 or the reporting engine 650 can discount anadvertising cost for an advertisement that aired on the channel definedby the channel tune and during a time period defined by the channel tunecluster pairs.

Conversely, if stage 1604 determines that the identified channel tunesrelated to a television processing device do not correspond to a channeltune cluster pair, then stage 1608 applies a full advertising cost foran advertisement that aired on the channel defined by the channel tuneand during a time period defined by the channel tune cluster pairs. Forexample, the accounting engine 130 or the reporting engine 650 can applya full advertising cost for an advertisement that aired on the channeldefined by the channel tune and during a time period defined by thechannel tune cluster pairs.

In another implementation, a confidence factor can be utilized to adjustan advertising cost. For example, a confidence factor of 100% can beassociated with a 33:00 minute dwell time beginning at 20:29:00, and thefull advertising cost can be applied; a confidence factor of 80% can beassociated with a 30:00 minute dwell time beginning at 20:30:00, and theadvertising cost can be adjusted to 80% of the full price; a confidencefactor of 70% can be associated with a 30:00 minute dwell time beginningat 20:29:00, etc. Multiple confidence factors can also be applied inproduct form, e.g., an 80% confidence factor can further scale a 60%adjustment of advertisements that likely recorded so that the coast ofthe advertisements are at 48% of the full price, etc.

FIG. 17 is a flow diagram of another example process 1700 foridentifying automatically generated channel tunes. The process 1700 can,for example, be implemented in the television advertising system 100 ofFIG. 1, or in the impression receiver engine 620, the automated tuningdetection engine 660, and the reporting recorder engine 650 of FIG. 6A.

Stage 1702 identifies triggering tune times. For example, the reportingengine 128 of FIG. 1, or the automated tuning detection engine 660 ofFIG. 6A can identify tune times that are often utilized by automatedrecording devices, such as programming start times and end times fortelevision programs, or rounded time values, e.g., 18:29:00, 18:30:00,18:31:00, etc.

Stage 1704 identifies tunes times corresponding to the triggering tunetimes. For example, the reporting engine 128 of FIG. 1, or the automatedtuning detection engine 660 of FIG. 6A can determine if tune times inthe reporting recodes correspond to triggering tune times, e.g., whethera first tune time for a particular television processing devicecorrespond to a triggering tune time of 19:29:00, 19:30:00, 19:31:00, orsome other triggering tune time.

Stage 1706 identifies the channel tunes occurring at the tunes timescorresponding to the triggering tune times as the automaticallygenerated channel tunes. For example, the reporting engine 128 of FIG.1, or the automated tuning detection engine 660 of FIG. 6A canidentifying a channel tune occurring at a tune time of 19:29:00 as anautomatically generated channel tune.

FIG. 18 is a flow diagram of another example process 1800 foridentifying automatically generated channel tunes. The process 1800 can,for example, be implemented in the television advertising system 100 ofFIG. 1, or in the impression receiver engine 620, the automated tuningdetection engine 660, and the reporting recorder engine 650 of FIG. 6A.

Stage 1802 identifies one or more tune times that occur at a firstcharacteristic times before a triggering tune time. For example, thereporting engine 128 of FIG. 1, or the automated tuning detection engine660 of FIG. 6A can identify tune times that occur at 19:29:00, 19:30:00,and 19:31:00 for a triggering tune time of 19:30:00.

Stage 1804 identifies one or more tune times that occur at a secondcharacteristic times after a triggering tune time. For example, thereporting engine 128 of FIG. 1, or the automated tuning detection engine660 of FIG. 6A can identify tune time of 20:01:00 for the triggeringtune time of 19:30:00, e.g., 31 minutes after the triggering tune timeof 19:30:00.

In some implementations, the reporting engine 128 of FIG. 1 or theautomated tuning detection engine 660 of FIG. 6A can determining if timeperiods defined by the tune times that occur at first characteristictune times and by the tune times that occur at the second characteristictune times correspond to dwell time clusters. Upon a positivedetermination, the channel tunes occurring at the tunes timescorresponding to the triggering tune times can be identified asautomatically generated channel tunes. For example, two consecutive tunetimes for a first set top box may be 19:32:00 and 19:59:00. While thesetune times may occur at characteristic times, the corresponding dwelltime of 27 minutes may not belong to a dwell time cluster. Accordingly,the tune times may have been viewer-triggered and by coincidenceoccurred at characteristic times. Accordingly, the corresponding channeltunes can be considered viewer-triggered.

Conversely, two consecutive tune times for a second set top box may be19:29:00 and 20:02:00. These tune times may occur at characteristictimes, and the corresponding dwell time of 32 minutes may also belong toa dwell time cluster. Accordingly, theses tune times are more likely tohave been automatically generated.

Other reporting data can also be processed to determine impressions asdescribed herein. For example, DVR-related reporting data can includeviewing time data, e.g., data indicating when DVR-rendered data appearedon a television display. The viewing time data can be mapped from anoriginal airtime to the delayed viewing time to determine whether animpression occurred.

While the system and methods have been described herein with respect toidentifying impressions for television advertisements, the systems andmethods describe herein can also be utilized to determine viewerbehavior for other contexts, such as behavior models in the context ofentry to commercial breaks, opening credits, closing credits, bottom ofthe hour commercial breaks, special broadcast interruptions, e.g., newsevents, sporting events extending beyond a scheduled broadcast time,etc. Such behavior models can, for example, be utilized to identifyoption content for presentation at certain times, e.g., 20-second“squeeze” advertisements rendered alongside closing credits performbetter than a full 60-second advertisement presented after the closingcredits, etc.

Additionally, the system and methods can also be utilized to performimpression processing for other content items, e.g., public serviceannouncements, special announcements, etc. Processing of impressions forcontent items in other media that provide reporting logs can also befacilitated, e.g., radio systems that provide log data either in-band(over an RF channel) or out-of-band (over a non-carrier channel, wirednetwork, etc.). For example, song impressions for a song on a radio canbe measured, e.g., whether users tune to another radio channel when asong was aired, etc.; or whether user recorded a radio program, etc.

The apparatus, methods, flow diagrams, and structure block diagramsdescribed in this patent document may be implemented in computerprocessing systems including program code comprising programinstructions that are executable by the computer processing system.Other implementations may also be used. Additionally, the flow diagramsand structure block diagrams described in this patent document, whichdescribe particular methods and/or corresponding acts in support ofsteps and corresponding functions in support of disclosed structuralmeans, may also be utilized to implement corresponding softwarestructures and algorithms, and equivalents thereof.

Embodiments of the subject matter described in this specification can beimplemented as one or more computer program products, i.e., one or moremodules of computer program instructions encoded on a tangible programcarrier for execution by, or to control the operation of, dataprocessing apparatus. The computer readable medium can be a machinereadable storage device, a machine readable storage substrate, a memorydevice, a composition of matter effecting a machine readable propagatedsignal, or a combination of one or more of them.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program does notnecessarily correspond to a file in a file system. A program can bestored in a portion of a file that holds other programs or data (e.g.,one or more scripts stored in a markup language document), in a singlefile dedicated to the program in question, or in multiple coordinatedfiles (e.g., files that store one or more modules, sub programs, orportions of code). A computer program can be deployed to be executed onone computer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described is this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. The computing systemcan include clients and servers. A client and server are generallyremote from each other and typically interact through a communicationnetwork. The relationship of client and server arises by virtue ofcomputer programs running on the respective computers and having aclient server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or of what may be claimed, but rather as descriptions offeatures that may be specific to particular embodiments of particularinventions. Certain features that are described in this specification inthe context of separate embodiments can also be implemented incombination in a single embodiment. Conversely, various features thatare described in the context of a single embodiment can also beimplemented in multiple embodiments separately or in any suitablesubcombination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can in some cases be excisedfrom the combination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

This written description sets forth the best mode of the invention andprovides examples to describe the invention and to enable a person ofordinary skill in the art to make and use the invention. This writtendescription does not limit the invention to the precise terms set forth.Thus, while the invention has been described in detail with reference tothe examples set forth above, those of ordinary skill in the art mayeffect alterations, modifications and variations to the examples withoutdeparting from the scope of the invention.

1. A computer-implemented method, comprising: receiving reporting datafrom a television provider; identifying channel tunes and correspondingtune times from the reporting data; identifying televisionadvertisements that were aired during the tune times on channelscorresponding to the channel tunes; identifying reliable durations basedon the identified channel tunes and corresponding tune times; anddetermining an impression value for each identified televisionadvertisement based on the reliable durations.
 2. The method of claim 1,comprising: identifying processing delays related to the tune times; andadjusting the tune times in response to the identified processingdelays.
 3. The method of claim 2, wherein: identifying processing delaysrelated to the tune times comprises: identifying a latency delay betweenan insertion time of a television advertisement at the televisionprovider and an air time of the television advertisement at a viewingdevice; and adjusting the tune times to compensate for the latencydelay.
 4. The method of claim 3, wherein: identifying a latency delaybetween an insertion time of an advertisement at the television providerand an air time of the advertisement at a viewing device comprises:identifying a fixed delay based on television provider encoder hardwareand software; and identifying a variable delay based on correspondingtune times for a channel tune.
 5. The method of claim 1, wherein:identifying reliable durations based on the identified channel tunes andcorresponding tune times comprises: determining if corresponding tunetimes for a channel tune define a duration that exceeds a minimum dwelltime threshold; and if the corresponding tune times for the channel tunedefine a duration that exceeds the minimum dwell time threshold, thenassociating the corresponding tune times for the channel tune as areliable duration.
 6. The method of claim 1, wherein: identifyingreliable durations based on the identified channel tunes andcorresponding tune times comprises: determining if corresponding tunetimes for a channel tune define a duration that is less than a maximumdwell time threshold; and if the corresponding tune times for thechannel tune define a duration that is less than the maximum dwell timethreshold, then associating the corresponding tune times for the channeltune as a reliable duration.
 7. The method of claim 1, wherein:identifying reliable durations based on the identified channel tunes andcorresponding tune times comprises: determining if a channel tune is anauthorized channel tune; and if the channel tune is not an authorizedchannel tune, then precluding association of the corresponding tunetimes for the channel tune as a reliable duration.
 8. The method ofclaim 1, wherein: identifying television advertisements that were airedduring the tune times on channels corresponding to the channel tunescomprises: identifying channels and associated advertisement time slots;identifying corresponding channel tunes and tune times; and identifyingtelevision advertisements associated with the identified channels andadvertisement time slots.
 9. The method of claim 8, wherein: determiningan impression value for each identified television advertisement basedon the reliable durations comprises identifying an impression for eachtelevision advertisement associated with an identified channel andadvertisement time slot that corresponds to a reliable duration; thereporting data defines a sampling weighting of a viewership; anddetermining an impression value for each identified televisionadvertisement based on the reliable durations comprises adjusting thesample weighting to compensate for a sample bias.
 10. The method ofclaim 9, wherein: determining an impression value for each identifiedtelevision advertisement based on the reliable durations comprises:determining a percentage of a maximum number of reports, the percentagedefined by the reporting data; and generating an estimated impressionvalue for each identified television advertisement based on percentageof the maximum number of reports and the number of identifiedimpressions for each television advertisement.
 11. The method of claim1, wherein: receiving reporting data from a television providercomprises: receiving digital video recorder data related to digitalvideo recorders.
 12. The method of claim 11, wherein: identifyingreliable durations based on the identified channel tunes andcorresponding tune times comprises: identifying recorded durations;determining whether the recorded durations have been played back afterrecording; and identifying recorded durations as not reliable if therecorded durations are determine to not have been played back afterrecording.
 13. The method of claim 12, wherein: determining whether therecorded durations have been played back after recording comprises:determining whether the recorded durations have been presented during acompressed playback; and identifying recorded durations as not reliableif the recorded durations are determine to have been presented during acompressed playback.
 14. The method of claim 3, wherein: identifying alatency delay between an insertion time of an advertisement at thetelevision provider and an air time of the advertisement at a viewingdevice comprises: identifying delays based on broadcaster insertionlocations.
 15. A system, comprising: an impression processor configuredto: receive impression records from a television provider and receivetelevision provider metadata, the impression records defining channelidentifiers, associated tuning events and associated tuning timesreported by tuning devices, and the television provider metadataincluding insertion times and intended display times of televisionadvertisements; and an impression filter configured to: compare theimpression records to filtering rules; and identify reliable impressionrecords based on the comparison.
 16. The system of claim 15, comprising:an impression analyzer configured to: correlate the reliable impressionrecords with advertisement insertion records to determine reportedimpressions for corresponding television advertisements; and estimateprojected impressions for the corresponding television advertisementsbased on the reported impressions.
 17. The system of claim 16, wherein:the impression analyzer is configured to: determine a percentage of amaximum number of impression records, the percentage defined by thenumber of received impression records; and estimate the projectedimpressions for the corresponding television advertisements based onpercentage of the maximum number of impression records and the reportedimpressions for the corresponding television advertisements.
 18. Thesystem of claim 17, wherein: the impression processor, impressionfilter, and the impression analyzer are configured to iteratively updatethe projected impressions during an impression time window based onsubsequently received impression records from the television providerduring the impression time window.
 19. The system of claim 18,comprising: an impression recorder configured to generate advertiserbillings based the projected impressions after the expiration of theimpression time window.
 20. The system of claim 15, wherein: thefiltering rules define a tuning event and an event time window; and theimpression filter is configured to determine whether the tuning evenoccurred during the event time window and identify an impression recordas a reliable impression record if the associated tuning event occurredduring the event time window.
 21. The system of claim 15 wherein: thefiltering rules define authorized channel identifiers associated withcorresponding impression records; and the impression filter isconfigured to preclude identification of an impression record as areliable impression record if the channel identifier of the impressionrecord is not one of the associated authorized channel identifiers. 22.A computer implemented method, comprising: receiving impression recordsfrom a television provider, the impression records defining channelidentifiers, associated tuning events and associated tuning timesreported by tuning devices; and filtering the impression records toidentify false positive impression records.
 23. The method of claim 22,comprising: correlating the impression records with advertisementinsertion records to determine reported impressions for correspondingtelevision advertisements; and estimating projected impressions for thecorresponding television advertisements based on the reportedimpressions and the identified false positive impression records. 24.The method of claim 22, comprising: defining an impression time window;iteratively processing impression records based on subsequently receivedimpression records from the television provider during the impressiontime window; and generating advertiser billings based on the projectedimpressions after the expiration of impression time window.
 25. Asystem, comprising: means for receiving reporting data from a televisionprovider, identifying channel tunes and corresponding tune times fromthe reporting data, and identifying reliable durations based on theidentified channel tunes and corresponding tune times; and means foridentifying television advertisements that were aired during the tunetimes on channels corresponding to the channel tunes and determining animpression value for each identified television advertisement based onthe reliable durations.